SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2526–2550 of 15113 papers

TitleStatusHype
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning—0
Average Reward Reinforcement Learning for Wireless Radio Resource Management—0
Average-reward model-free reinforcement learning: a systematic review and literature mapping—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks—0
Average-Reward Learning and Planning with Options—0
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
Average Reward Adjusted Discounted Reinforcement Learning: Near-Blackwell-Optimal Policies for Real-World Applications—0
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning—0
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference—0
A Benchmark for Low-Switching-Cost Reinforcement Learning—0
Control of Memory, Active Perception, and Action in Minecraft—0
Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning—0
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints—0
A Mixture-of-Expert Approach to RL-based Dialogue Management—0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
A Mini Review on the utilization of Reinforcement Learning with OPC UA—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting—0
A Validation Tool for Designing Reinforcement Learning Environments—0
Adaptive Policy Transfer in Reinforcement Learning—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified